After weeks of training and optimizing a neural net at some point it might be ready for production. Most deep learning projects never reach this point and for the rest it’s time to think about frameworks and technology stack. The…

There is a lot of confusion about return_state in Keras. What does ist actually return and how can we use it for stacking RNNs or encoder/decoder models. Hopefully this post makes it a bit clearer. Cell state vs Hidden state…

This post is about how to snapshot your model based on custom validation metrics. First we define the custom metric, as shown here. In this case we use the AUC score: import tensorflow as tf from sklearn.metrics import roc_auc_score def…

Keras offers some basic metrics to validate the test data set like accuracy, binary accuracy or categorical accuracy. However, sometimes other metrics are more feasable to evaluate your model. In this post I will show three different approaches to apply…